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PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.
Check out the getting started guide <https://docs.pymc.io/en/stable/pymc-examples/examples/getting_started.html>, or
interact with live examples <https://mybinder.org/v2/gh/pymc-devs/pymc/main?filepath=%2Fdocs%2Fsource%2Fnotebooks>
using Binder!
For questions on PyMC, head on over to our PyMC Discourse <https://discourse.pymc.io/>__ forum.
x ~ N(0,1)
translates to x = Normal('x',0,1)No U-Turn Sampler <http://www.jmlr.org/papers/v15/hoffman14a.html>__, allow complex models
with thousands of parameters with little specialized knowledge of
fitting algorithms.ADVI <http://www.jmlr.org/papers/v18/16-107.html>__
for fast approximate posterior estimation as well as mini-batch ADVI
for large data sets.Aesara <https://aesara.readthedocs.io/en/latest/>__ which provides:
API quickstart guide <https://docs.pymc.io/en/stable/pymc-examples/examples/pymc3_howto/api_quickstart.html>__PyMC tutorial <https://docs.pymc.io/en/stable/pymc-examples/examples/getting_started.html>__PyMC examples <https://docs.pymc.io/nb_examples/index.html> and the API reference <https://docs.pymc.io/en/stable/api.html>Probabilistic Programming and Bayesian Methods for Hackers <https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers>__: Fantastic book with many applied code examples.PyMC port of the book "Doing Bayesian Data Analysis" by John Kruschke <https://github.com/aloctavodia/Doing_bayesian_data_analysis> as well as the second edition <https://github.com/JWarmenhoven/DBDA-python>: Principled introduction to Bayesian data analysis.PyMC port of the book "Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath <https://github.com/pymc-devs/resources/tree/master/Rethinking>__PyMC port of the book "Bayesian Cognitive Modeling" by Michael Lee and EJ Wagenmakers <https://github.com/pymc-devs/resources/tree/master/BCM>__: Focused on using Bayesian statistics in cognitive modeling.Bayesian Analysis with Python <https://www.packtpub.com/big-data-and-business-intelligence/bayesian-analysis-python-second-edition> (second edition) by Osvaldo Martin: Great introductory book. (code <https://github.com/aloctavodia/BAP> and errata).YouTube playlist <https://www.youtube.com/playlist?list=PL1Ma_1DBbE82OVW8Fz_6Ts1oOeyOAiovy>__ gathering several talks on PyMC.here <https://discourse.pymc.io/c/pymcon/2020talks/15>__."Learning Bayesian Statistics" podcast <https://www.learnbayesstats.com/>__ helps you discover and stay up-to-date with the vast Bayesian community. Bonus: it's hosted by Alex Andorra, one of the PyMC core devs!To install PyMC on your system, follow the instructions on the appropriate installation guide:
Installing PyMC on MacOS <https://github.com/pymc-devs/pymc/wiki/Installation-Guide-(MacOS)>__Installing PyMC on Linux <https://github.com/pymc-devs/pymc/wiki/Installation-Guide-(Linux)>__Installing PyMC on Windows <https://github.com/pymc-devs/pymc/wiki/Installation-Guide-(Windows)>__Please choose from the following:
Releases <https://github.com/pymc-devs/pymc/releases>_.. |DOIpaper| image:: https://img.shields.io/badge/DOI-10.7717%2Fpeerj--cs.55-blue :target: https://doi.org/10.7717/peerj-cs.55 .. |DOIzenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4603970.svg :target: https://doi.org/10.5281/zenodo.4603970
We are using discourse.pymc.io <https://discourse.pymc.io/> as our main communication channel. You can also follow us on Twitter @pymc_devs <https://twitter.com/pymc_devs> for updates and other announcements.
To ask a question regarding modeling or usage of PyMC we encourage posting to our Discourse forum under the “Questions” Category <https://discourse.pymc.io/c/questions>. You can also suggest feature in the “Development” Category <https://discourse.pymc.io/c/development>.
To report an issue with PyMC please use the issue tracker <https://github.com/pymc-devs/pymc/issues>__.
Finally, if you need to get in touch for non-technical information about the project, send us an e-mail <pymc.devs@gmail.com>__.
Apache License, Version 2.0 <https://github.com/pymc-devs/pymc/blob/main/LICENSE>__
Bambi <https://github.com/bambinos/bambi>__: BAyesian Model-Building Interface (BAMBI) in Python.SunODE <https://github.com/aseyboldt/sunode>__: Fast ODE solver, much faster than the one that comes with PyMC.pymc-learn <https://github.com/pymc-learn/pymc-learn>__: Custom PyMC models built on top of pymc3_models/scikit-learn APIfenics-pymc3 <https://github.com/IvanYashchuk/fenics-pymc3>__: Differentiable interface to FEniCS, a library for solving partial differential equations.Exoplanet <https://github.com/dfm/exoplanet>__: a toolkit for modeling of transit and/or radial velocity observations of exoplanets and other astronomical time series.NiPyMC <https://github.com/PsychoinformaticsLab/nipymc>__: Bayesian mixed-effects modeling of fMRI data in Python.beat <https://github.com/hvasbath/beat>__: Bayesian Earthquake Analysis Tool.cell2location <https://github.com/BayraktarLab/cell2location>__: Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics.Please contact us if your software is not listed here.
See Google Scholar <https://scholar.google.de/scholar?oi=bibs&hl=en&authuser=1&cites=6936955228135731011>__ for a continuously updated list.
See the GitHub contributor page <https://github.com/pymc-devs/pymc/graphs/contributors>. Also read our Code of Conduct <https://github.com/pymc-devs/pymc/blob/main/CODE_OF_CONDUCT.md> guidelines for a better contributing experience.
PyMC is a non-profit project under NumFOCUS umbrella. If you want to support PyMC financially, you can donate here <https://numfocus.salsalabs.org/donate-to-pymc3/index.html>__.
PyMC is now available as part of the Tidelift Subscription!
Tidelift is working with PyMC and the maintainers of thousands of other open source projects to deliver commercial support and maintenance for the open source dependencies you use to build your applications. Save time, reduce risk, and improve code health, while contributing financially to PyMC -- making it even more robust, reliable and, let's face it, amazing!
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You can also get professional consulting support from PyMC Labs <https://www.pymc-labs.io>__.
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